Automated systems may use video analytics to process video data to determine object movements and other events of concern. Such systems may use background subtraction (BGS) mask processes to distinguish object items as foreground features in the video data, objects that are distinct from a background model of the image data and discernible within video imagery. The primary goal of automated video surveillance is generally to detect and raise alerts for abnormal or other events that fit defined descriptions of concern. When determined object conditions or behaviors are met, alerts are issued to human monitors for adjudication. Examples of alert triggers include detecting a bag object remaining unmoving for a specified period of time in the case of possible abandoned objects, an automobile-sized object stationary and across railway tracks in a railway crossing scene, a person object located outside of a safety barrier, and still others will be apparent to one skilled in the art. The object alerts are typically issued to human monitors to manually review the video data or physically respond to a scene itself to inspect the object in order to decide whether the alarm is true, or that it is false and that no object behavior of concern is actually indicated by the foreground features.
Though effective in determining object behaviors meeting thresholds for raising user-specified concerns, large-scale deployments of computer-vision based systems typically generate such a large number of false alarms thought automated processes that human verification of each alarm may be impractical. For example, a system that processes video data input and yields an average of four alerts per day per camera will produce a total of 4,000 alerts per day from 1,000 cameras. Assuming that each alert may be verified in an average time of two minutes indicates that 66.67 man-power hours will be required to adjudicate an average number of alerts generated in a 24-hour day: this is equivalent to the workload of a team of more than eight full-time employees. Accordingly, human adjudication costs may be prohibitive and indicate against deploying automated video surveillance systems having large numbers of cameras.